Distributed Adaptive Sampling Using Bounded-Errors

This paper presents a communication/coordination/ processing architecture for distributed adaptive observation of a spatial field using a fleet of autonomous mobile sensors. One of the key difficulties in this context is to design scalable algorithms for incremental fusion of information across platforms robust to what is known as the “rumor problem”. Incremental fusion is in general based on a Bayesian approach, and algorithms (e.g. the Covariance Intersection, CI) which propagate consistent characterizations of the estimation error under this challenging situation have been proposed. In this paper, we propose to base inter-sensor fusion on a deterministic approach which considers that bounds to the observation errors are known, wich is intrinsically robust to the rumor problem. We present the equations that enable the determination of the ellipsoidal domain of uncertainty that covers the intersection of the individual sets describing sensor’s uncertainty, and show that they solve some pathologies associated to CI. The results presented corroborate a previous claim of the robustness of our control strategy (the criterion used for adaptively choosing the nodes positions) with respect to the conservativeness of fusion methods able to handle rumor